|
|
![]() |
|
4.2 DefinitionsThe underlying assumption of all forms of stock market picking is that the picker knows news or information that is not known to the millions of other market participants. For continued success, the picker must have a never-ending source of information not available to all other traders. Let it go! No mortal can single-handedly possess such incredibly powerful and immensely valuable information. Two concepts that support the concept that timers are unable to pick the right times to invest are the Random Walk Theory and the Efficient Market Hypothesis. 4.2.1Random Walk TheoryThe Random Walk Theory essentially states that there are no discernible patterns in stock market prices. The logic and reasoning goes like this. News moves the markets. News is both unpredictable and random by definition. At the moment of discovery, the new knowledge or information is no longer new and quickly becomes old news. Since free financial markets are free of constraints, this new information is continuously reflected in the prices of relevant financial instruments. Therefore, the world's markets move in a random and unpredictable manner. Over time the distribution of returns form a near bell shaped curve. See several probability machines that simulate monthly returns of the market here:
As an example of randomness, look at these Wall Street Journal market summaries:
Of course there is a positive upward movement over 15 to 20-year periods in diversified portfolios due to the compensation that investors receive for subjecting their capital to risk. The higher levels of the right risk factors correlate to higher expected returns over long periods of time. But the positive upward movement is virtually invisible when looking at returns over smaller periods of minutes, hours, days, months, or even several years. This positive movement is so small that Nobel Laureate Paul Samuelson compares it to watching grass grow. Go out in a big field and take a look. As a side note, the reason markets trend upward is that the sun shines on capitalism, as your cash provides the fuel to fund profitable ventures. Your cash can be injected into the market through the purchase of products, services, debts or equities. On average, this free market system works better than a central government controlled system. Communism still exists in only a few countries where there is a mentality similar to that of active investors. This mentality is based on the falsehood that free markets do not reflect all information. Market speculators and communists both think they know more than the collective opinion of millions of voting market participants. They assume that they possess information that has not yet been picked up by the radar of all traders throughout the world. On the other hand, indexers invest under the assumption that markets properly price assets and risk. Rex Sinquefield is the co-founder and a director of Dimensional Fund Advisors. He is also one of the world's foremost experts on the stock market. In 1995, he was asked to represent index funds investing in a debate with an active manager at a Schwab conference. After an eloquent review of the history of capital markets from Adam Smith to Eugene Fama, he threw down the gauntlet to a room full of active managers, So, who still believes that markets don't work? Apparently it is only the North Koreans, the Cubans, and the active managers. 4.2.2Efficient Market HypothesisThe efficiency of communication has progressed as follows: horseback, slow boat, smoke signals, homing pigeons, flashing lights on navy ships, Morse code, telegraphs, telephones, radios, televisions, computer networks, and finally the Internet. With each step, information and news became cheaper, more accurate, and more rapidly disseminated. The
Efficient Market Hypothesis simply states that market prices accurately
reflect all available information at all times. This leads to the conclusion
that it is impossible to consistently beat the market averages. As
Bachelier stated in 1900, the expected return of speculation is zero.
The most recent studies by Richard Roll indicate that new information
is reflected in market prices within five to sixty minutes. Within
that sixty minutes there are hundreds or thousands of traders all competing
to profit from the information. If you are in charge of one billion
dollars, a 0.1% annual gain is worth one million dollars per year.
Consequently, managers of those funds are applying considerable resources
to squeeze out every little gain from new information. For this simple
reason alone, there is an absence of opportunities for one trader to
consistently profit from all other traders who have access to the same
information at the same time! In short, all
of us know more than any one of us and it is impossible for one person
to consistently possess more knowledge than all the other traders combined. From the DFA web site, on their FAQs page: In layman's terms, what is the Efficient Market Hypothesis? The Efficient Market Hypothesis says that market prices are fair: they fully reflect all available information. This does not mean that prices are perfect; some prices may be too high and some too low, but there is no reliable way to tell. In an efficient market, investors cannot expect to earn above-average profits without assuming above-average risks. Market efficiency does not suggest that investors can't "win." Over any period of time, some investors will beat the market, but the number of investors who do so will be no greater than expected by chance. More from DFA on Efficient Markets"The efficient markets hypothesis (EMH) is an organizing principle for understanding how markets work and what investors should care about. Professor Eugene F. Fama of the University of Chicago performed extensive research on stock price patterns. In 1966, he developed the efficient markets hypothesis, which asserts that:
Viewing the markets as efficient has important implications. If current market prices offer the best available estimate of intrinsic value, stock mispricing should be regarded as a rare condition that cannot be systematically exploited through analysis and forecasting. Moreover, if new information is the main driver of prices, only unexpected events will trigger price changes. This may be one reason that stock prices seem to behave randomly over the short term. The EMH implies that no investor will consistently outperform the stock market except by chance, and that all investors may be best served through passively structured portfolios. Rather than trying to out-research other market participants, a passive investor looks to asset class diversification to manage uncertainty and position for long-term growth in the capital markets. Market efficiency argues that when securities become mispriced, market forces quickly push prices back toward fair value. This equilibrium does not depend on all investors having the same information or level of expertise. It only requires that many intelligent participants have information. No single investor will have all the information or know how to use it. In fact, no single investor can possibly have all the information, as it will be scattered among many participants who are all competing to maximize their potential profit as buyers and sellers. The market mechanism gathers the information, evaluates it, and builds it into prices.
It may be hard to conceive current stock prices as rational, especially when markets are extremely volatile. The EMH does not claim that markets are always rational or correctly factor information into prices. The only condition required is that a large number of market participants don't consistently exploit price differences to outperform the market average. Also, market efficiency does not rule out the possibility that some investors will earn above-normal returns. Over any period of time, some investors will beat the market, but the number of investors who do so will be no greater than expected by chance."
The job of the free market is to set prices so that investors are compensated for the risk they bear. Investors should be confident that every future period (day, month, year, 5, or 10 years), has approximately the same expected return (Er) for that period, given a certain investment (i). In other words, for each investment there is some probability distribution of future returns, where the average or mean of that distribution is the expected return. The expected return and standard deviation for diversified portfolios can be best estimated by looking at the last 50 years or using the Fama/French Five Factor Model for equities and fixed income, as explained here. Investors should assume that the expected return is essentially constant based on the Five Factor Model or a long term (approximately 50 years) historical annualized return and standard deviation of a given investment, regardless of market conditions. The expected return changes very slightly as we add one data point at the end and drop one data point from the front of the historical data, to get the new average or annualized return and standard deviation. Stated as a formula, the Current Price of an Investment (Pi) equals the current Expected Return (Eri) divided by the market's assessment of the current Uncertainty of that Expected Return (UEri), which we now call the Hebner Model: As illustrated in Figure 4 below, news (uncertainty), prices and returns are generated in a random series as time goes by, but within ranges (standard deviations) that are tied to the risk level of Index Portfolio 50. IFA suggests that investors who score a 50 on the Risk Capacity Survey should invest in Index Portfolio 50 and that they should have an average holding period of about 7 years. Figure 4 estimates 7 years (84 months) of news, prices and monthly returns. The 84 monthly returns are simulated by the dropping of beads from the center of the folcrum. The beads eventually form a bell shape curve, with a shape that resembles what was expected: an Average Return of near 0.9% /month and a Standard Deviation (2.5%). These characteristics are appropriate for Index Portfolio 50, based on 600 monthly returns. The 600 months of data are represented by the black outline of the distribution in the folcrum. This bell curve is our best estimate of the probability distribution of future returns or Eri. Figure 4: As Time Goes By - The price agreed to by willing buyers and sellers embeds the expected return and the uncertainty of it for that moment in time. For this reason, investors can expect to be properly compensated for the risks they accept, every day they buy, regardless of price or market conditions because a free market reaches a price that is an equilibrium point between the two factors. Don't forget that the greater the risk, or volatility, of the investment, the longer the investor should be prepared to wait to achieve their annualized expected return. It is time, not timing that will determine your investing success. As long as markets are free to trade, Adam Smith's invisible hand should work. The best assumption for investors is to assume that prices are fair at all times. Fair prices are prices where investors are properly compensated for the risk they bear over a reasonable period of time. If you think the price is wrong, you won't know for sure until long after the fact. In
Robert C. Higgins book, Analysis for Financial Management, he paints
a vivid picture of how information is devoured by market participants: "Market
efficiency is a description of how prices in competitive markets respond
to new information. The arrival of new information to a competitive market
can be likened to the arrival of a lamb chop to a school of flesh-eating
piranha, where investors are--plausibly enough--the piranha.
The instant the lamb chop hits the water, there is turmoil as the fish devour the meat. Very soon the meat is gone, leaving only the worthless bone behind, and the water returns to normal. Similarly, when new information reaches a competitive market there is much turmoil as investors buy and sell securities in response to the news, causing prices to change. Once prices adjust, all that is left of the information is the worthless bone. No amount of gnawing on the bone will yield any more meat, and no further study of old information will yield any more valuable intelligence." Benjamin Graham is the most famous of all stock pickers. Ultimately, even he agreed with the efficient market theory as seen in this video clip on the left. Eugene Fama's paper Market Efficiency, Long-Term Returns, and Behavioral Finance is the #1 downloaded academic paper on the web and explains the most recent challenges to this hypothesis. 4.3 Problems4.3.1 Pickers are Fooled by RandomnessTo save you some time,
all you need to understand about time picking is the Random Walk Theory. This theory simply states that nobody can consistently see what tomorrow will bring.
Just remember that markets are moved by news--news that is unpredictable and unknowable in advance (that is the very definition of "news"). Because news is random and unpredictable, the markets move in a random and unpredictable
fashion. Period, end of story. They also determined
that if a picker called one hundred percent of the declining markets
and only fifty percent of the rising markets, they still would fail
to exceed the return of the overall market during this period. To add
a final blow, there was no consideration for the higher short-term capital
gains taxes or transaction costs involved in this highly flawed strategy.
No wonder ninety-five percent of market timing newsletters go out of
business. Missing the Best and Worst DaysAlmost all big stock market gains and drops are concentrated in just a few trading days each year. Missing only a few days can have a dramatic impact on returns. Table 4-1a illustrates how an investor who hypothetically remained invested in the S&P 500 Index throughout the 20-year period from 1991 to 2010 (5,043 trading days) would have earned a sizable 9.14% annualized return, growing a $10,000 investment to $57,512. By missing only the five best-performing days in that time period, annualized return shrank to 6.93%, with $10,000 growing to $38,167. Even worse, if an investor missed just the one day a year (on average) with the largest gains, the returns were cut down to just 2.99% a year. If an average of just two of the
biggest days a year were missed, an investment in the S&P 500 turned negative, with $10,000 eroding in value to just $8,243, a loss of $1,757!
Table 4-1a ![]() Table 4-1b ![]() Big down days and big up days frequently come right next to each other. This is volatility—and it is why you have to stay in the markets to get the markets’ superior return. The chart below shows the returns of the IFA Index Portfolios in the year that followed the bottoming of the market on March 9, 2009. One year later, to the day, we see that investors who pulled out of the market in early 2009 and remained terrified about getting back in, missed out on a 102.52 % gain on our IFA Index Portfolio 90, which is IFA’s full-equity portfolio. Figure 4-1a To better visualize just how hard it is to find those 20 days, here is an image of the whole period in the study.
Professor Hersh Shefrin took a look at some very interesting behavioral finance issues about investor's perceptions of how markets works. In surveys of both individual and professional investors, he discover that neither one understood that last year's return had no predictive value for the next year. Individuals tended to think there was a positive correlation, meaning that one bad year is followed by another bad year and one good year is followed by a good year. Professional investors tended toward the opposite point of view, thinking that one good year tends to be followed by a bad year and visa versa. The fact is that they are both wrong. As indicated by the very low R2 values in Figures 4a-4d below, no previous period was a predictor of the subsequent period. Shefrin also explores the investors lack of understanding of how risk has a positive correlation to returns, meaning more risk begets more return over the longer periods like 10 years. He stated, "...investors
have a good sense of what makes up risk, but a poor sense of
how to connect that to expected returns." (see his paper, Behavioral Finance,
by Hersh Shefrin, CFA Institute, June 2007, pages 1-7) ![]() ![]() Figure 4-2b1 ![]() ![]() ![]() Figure 4-2e ![]() ![]() Figure 4-2g ![]() ![]() 4.3.2 Academic Studies Prove that Time Picking Doesn't WorkThe literature is full of studies confirming the failure of market timing. All these peer- reviewed research papers share the same conclusion. Forget about trying to time the market. In the paper entitled, "Selectivity and Market Timing Performance of Fidelity Sector Mutual Funds," Dellva, Demaskey and Smith concluded that there was negative timing ability among the Fidelity sector funds during the period from 1989 to 1998. In 1994, Graham and Harvey, both distinguished professors at Duke University, studied 237 investment newsletters over the 1980-1992 period. As was stated in the introduction, they concluded that "there is no evidence that newsletters can time the market. Consistent with mutual fund studies, winners rarely win again and losers often lose again." In 1998, Becker, Ferson, Myers, and Schill studied market timing in their paper entitled, "Conditional Market Timing with Benchmark Investors." They found no evidence supporting the claim that funds have significant market timing ability. Wei Jiang presents his market timing studies in his 2001 paper, "A Nonparametric Test of Market Timing." After spending countless hours combing through the results of 1,557 retail mutual funds and 210 institutional funds, Jiang concluded that timing ability on average is negative. Just as a side note, this paper lists 41 other academic studies in the reference section, providing further corroboration that market timing doesn't work. Super star academic William Goetzmann, along with Ingersoll and Ivkovich, put in their two cents with a paper entitled, "Monthly Measurement of Daily Timers." They performed four tests of timing skill on a sample of 558 mutual funds. They concluded that very few funds exhibit statistically significant timing skill. In another paper written in 2002 by Johannes, Polson and Stroud, market timing was once again put to the test. Their simple yet powerful conclusion was that market timing strategies performed worse than the buy-and-hold strategy in all cases they examined. To
illustrate the extreme concentration of stock market returns, H. Nejat
Seyhun carefully analyzed the 7,802 trading days for the 30 years from
1963 to 1993. Mr. Seyhum is the Chairman of Finance at the University
of Michigan School of Business Administration, a position that is only
achieved by highly dedicated and intelligent individuals who have spent
many years learning how capital markets work. His conclusion provides
a crushing blow to timers who think they can outsmart the market. A mere
90 days over 30 years contained 95% of all the market gains. That is an
average of 3 days per year. In summary, all of the above studies demonstrated that there is no evidence that time pickers can consistently know where the market is headed. 4.3.3Time Picking Gurus
How often does a market-timing guru need to be right to beat an index? Nobel Laureate William Sharpe set out to answer that very question in his 1975 study titled, "Likely Gains from Market Timing.("(William Sharpe, "Likely Gains from Market Timing,") Financial Analysts Journal, vol. 31, no 2 (1975). Sharpe wanted to identify the percentage of time a market timer would need to be correct to break even relative to a benchmark portfolio. He concluded a market timer must be correct 74% of the time in order to outperform a passive portfolio at a comparable level of risk. In 1992, SEI Corporation updated Sharpe's study to include the average 9.4% stock market return from the period 1901 – 1990. This study determined that gurus must be right at least 69% and as high as 91% of the time, depending on the timing of the moves. ("Technical Note: Calculation of Forecasting Accuracy," SEI Corporation position paper, April 1992.) What percentage of times do market timing gurus get it right? CXO Advisory Group tracks public forecasts of self-proclaimed market-timing gurus and rates their accuracy by assigning grades as "correct," "incorrect" or "indecisive." The chart depicts CXO's percentage grades for 37 well-known market-timing gurus who made a collective 3,541 forecasts from as early as December 28, 1998 through August 15, 2011. The study shows that not one of the self-proclaimed gurus were able to meet Sharpe's requirement of 74% accuracy, or SEI's minimum 69%, thereby failing to deliver accuracy sufficient to beat a simple index portfolio. Forecast AccuracyAt first glance, the 11 gurus who had percentage accuracy of more than 50% might look appealing to a time picker – but beware, the opportunity costs associated with a time picker's proclivity toward holding cash in some up years creates a higher hurdle as they will have to make up those superior returns foregone by stocks. Transaction costs associated with market timing add another hurdle for market timers to break even. The pied pipers of Wall Street do not have a good batting average. No Babe Ruths here. Smartmoney.com has been tracking the pundits dating back to 1997. The table below summarizes the batting averages of several of these market pundits. You can see that Ed Hyman has the best batting average with a 0.236 and Ed Hyman is considered one of the best economists. That is the equivalent of hitting an average double each time at bat. Or in the scoring system, a call that may win plaudits for accuracy but not for insight and strong feeling. Often a general statement that comes true. For example, the forecaster made a correct but obvious and wishy-washy call about the direction of interest rates. For those who scored lower, it generally means a true dud of a pick or a mostly inaccurate prediction that might have one redeeming feature but that likely fails the degree of difficulty and/or confidence tests. A batting average of 0.400 would indicate an accurate forecast that was difficult to make but still uttered with the utmost confidence. Here are the averages as reported by Smartmoney, where the the average batting average for all 12 forecasters was somewhere between first and second base, 0.166. Since an accurate call would yield an average of 0.400, the most repected and well known market forecaster fall on the side of inaccuracy about 60% of the time and accuracy about 40% of the time.
For
further evidence of the heavy fog in crystal balls, let’s take a
look at results from 2003 and predictions about them. In that year, stock
prices rose in almost every global market. Returns for U.S. small company
stocks were particularly strong; the total return for the Russell 2000
Index was 47.25%, the highest annual return since inception of the index
in 1979, according to Russell Analytic Service; and the total return for
the CRSP 9-10 Index was in excess of 70%, the highest annual return since
1967, according to the Center for Research in Security Prices, University
of Chicago. Figure 4-2i Since most investors only hear about the forecasts there were correct, here is an offset to that point of view. Most predictions are wrong, as seen in the many examples below.
The year 2006 was another good year for investors around the globe as equity prices rose in 46 of the 50 countries whose equity market returns are reported by Morgan Stanley Capital International. Among these, only Israel, Jordan, Thailand, and Turkey saw their local stock market indexes slump for the year. Total return for United States stocks was 15.32% according to MSCI, placing it next-to-last among 23 developed markets (in dollar terms) and 42nd out of 50 countries in all. There were 36 markets with a total return greater than 20% (in dollar terms), and 19 had a total return greater than 40%. Nine of the top ten were emerging markets. (MSCI data, copyright MSCI 2006, all rights reserved.) To capture the returns of equity markets, investors require a better understanding of market timing and capitalism than that of the so-called experts. A review of the many market guru predictions and other news events suggested that 2006 would be less profitable than what happened. Reading these should help you resist the temptation to alter your portfolio based on the coming market predictions for 2007. Investors are far better off to focus on the risk of their portfolios, and let the returns ebb and flow with the news about capitalism. Free markets were meant to be free... not managed.
See more predictions from 2007.- The Press and Its Poor Market Timing If
the highly paid experts can't get it right, then who can predict the near term direction of
markets? Answer: Nobody.
One example of an interesting period would be a view of the 1929 stock market crash period in an Index Portfolio 100. Or take a look at a more recent 6 year period (1969-1974) where Index Portfolio 100 was still down 34% after 6 long years, but that is why this level of risk is designed for 12 years or more time horizons. Or 6 years from 1975-1980, where it went up 363% total return.
|
|||||||||||||||||||||||||||||||||||||
|